Python Data Analysis with JupyterLab

If you are using or plan to use Python for data science or data analytics, then this is the right Python course for you. This course is in-depth and assumes that you already possess a strong understanding of Python from previous training or experience.

Details
Syllabus
Requirements

Enrollment Options

$119.00

Self-Guided

3 Months Access

No Instructor. Course begins when you enroll.

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$119.00

Self-Guided

Enroll Now

Details

You will learn how to use Jupyter Notebook, an essential tool for writing, testing, and sharing quick Python programs. As the course progresses, you will also learn about Python libraries such as NumPy, which makes working with arrays and matrices more efficient, and pandas, a key tool for manipulating, munging, slicing, and grouping data. The course will conclude with an overview of simple data visualization techniques with matplotlib.

Syllabus

JupyterLab
1. Exercise: Creating a Virtual Environment
2. Exercise: Getting Started with JupyterLab
3. Jupyter Notebook Modes
4. Exercise: More Experimenting with Jupyter Notebooks
5. Markdown
6. Exercise: Playing with Markdown
7. Magic Commands
8. Exercise: Playing with Magic Commands
9. Getting Help
NumPy
1. Exercise: Demonstrating Efficiency of NumPy
2. NumPy Arrays
3. Exercise: Multiplying Array Elements
4. Multi-dimensional Arrays
5. Exercise: Retrieving Data from an Array
6. More on Arrays
7. Using Boolean Arrays to Get New Arrays
8. Random Number Generation
9. Exploring NumPy Further
pandas
1. Getting Started with pandas
2. Introduction to Series
3. np.nan
4. Accessing Elements in a Series
5. Exercise: Retrieving Data from a Series
6. Series Alignment
7. Exercise: Using Boolean Series to Get New Series
8. Comparing One Series with Another
9. Element-wise Operations and the apply() Method
10. Series: A More Practical Example
11. Introduction to DataFrames
12. Creating a DataFrame using Existing Series as Rows
13. Creating a DataFrame using Existing Series as Columns
14. Creating a DataFrame from a CSV
15. Exploring a DataFrame
16. Exercise: Practice Exploring a DataFrame
17. Changing Values
18. Getting Rows
19. Combining Row and Column Selection
20. Boolean Selection
21. Pivoting DataFrames
22. Be careful using properties!
23. Exercise: Series and DataFrames
24. Plotting with matplotlib
25. Exercise: Plotting a DataFrame
26. Other Kinds of Plots

Requirements

Prerequisites:

Prior to enrolling in this course, you must have previous Python programming experience. You should be comfortable writing your own functions and working with strings, lists, tuples, dictionaries, loops, and conditionals.

Requirements:

Hardware Requirements:

This course can be taken on either a PC or Mac.

Software Requirements:

PC: Windows 10 or later.
Mac: macOS 11.0 or later.
Browser: The latest version of Google Chrome or Mozilla Firefox are preferred. Microsoft Edge and Safari are also compatible.
Adobe Acrobat Reader.
Software must be installed and fully operational before the course begins.

Other: